Supervised ensemble classification of Kepler variable stars

Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. We present an expansion of existing work on the field by analysing variable stars in the Kepler field using an ensemble approach, combining multiple characterization and classification techniques to produce improved classification rates. Classifications for each of the roughly 150 000 stars observed by Kepler are produced separating the stars into one of 14 variable star classes. The study of variable stars has provided a wealth of valuable astrophysical information. Intrinsic sources of variation, such as in pulsation, provide a physical probe and test for our understanding of stellar atmospheres and interiors.